Reconstruction of Black Hole Ringdown Signals with Data Gaps using a Deep-Learning Framework

TL;DR

This paper presents DenoiseGapFiller, a deep-learning framework that effectively reconstructs gravitational-wave ringdown signals with data gaps and noise, improving signal fidelity for better detection and parameter estimation.

Contribution

Introduction of DenoiseGapFiller, a novel deep-learning model with a dual-branch encoder-decoder architecture for reconstructing noisy, gapped gravitational-wave signals.

Findings

Achieves a mean waveform mismatch of 0.002

Reduces residual amplitudes by an order of magnitude

Suppresses spectral density in key frequency band by 1-2 orders

Abstract

We introduce DenoiseGapFiller (DGF), a deep-learning framework specifically designed to reconstruct gravitational-wave ringdown signals corrupted by data gaps and instrumental noise. DGF employs a dual-branch encoder-decoder architecture, which is fused via mixing layers and Transformer-style blocks. Trained end-to-end on synthetic ringdown waveforms with gaps up to 20% of the segment length, DGF can achieve a mean waveform mismatch of 0.002. The residual amplitudes of the Time-domain shrink by roughly an order of magnitude and the power spectral density in the 0.01-1 Hz band is suppressed by 1-2 orders of magnitude, restoring the peak of quasi-normal mode(QNM) in the time-frequency representation around 0.01-0.1 Hz. The ability of the model to faithfully reconstruct the original signals, which implies milder penalties in the detection evidence and tighter credible regions for parameter estimation, lay a foundation for the following scientific work.